IDENTIFICATION AND ANALYSIS OF ATYPICAL FACTORS AFFECTING THE DURATION OF CONSTRUCTION PROJECTS

The study's relevance is determined by the growing influence of atypical, low-predictability factors on the duration of construction projects under conditions of socio-economic and regulatory instability. In a dynamic environment marked by frequent changes, traditional scheduling approaches are proving insufficient, as they fail to consider the impact of crises, regulatory shifts, social disruptions, and geopolitical risks. The lack of methodological tools for systematically including such factors limits the effectiveness of time management in construction project implementation. The aim of the article is to provide a scientific rationale for identifying and assessing atypical factors affecting the duration of construction projects in order to improve planning efficiency and timeline management under dynamic conditions. The methodological framework is based on the integration of systems thinking, contextual analysis, scenario-based planning, and comparative evaluation, enabling the identification of atypical influence types, the analysis of current scientific approaches to their assessment, and the development of a conceptual model for their integration into project planning. The study proposes a typology of atypical factors according to their origin (social, regulatory, climatic, geopolitical, reputational) and analyzes the effectiveness of classical, adaptive, contextual, and data-driven approaches to their inclusion. The proposed model allows for the incorporation of multi-scenario planning as a proactive measure to mitigate the impact of unexpected disruptions. Additionally, the integration of contextual indicator monitoring serves as a dynamic feedback mechanism for timely adjustment of project schedules. The conceptual framework also emphasizes the importance of digital maturity in implementing adaptive management strategies within construction project environments. A conceptual model is developed, incorporating time buffer strategies, multi-scenario planning, and contextual indicator monitoring. Key limitations of implementing a comprehensive analysis of atypical factors are identified, including the absence of a unified classification, limited digital maturity, fragmented data, and institutional inertia. It is demonstrated that the proposed model enhances forecasting accuracy, management flexibility, and project resilience in the face of external disruptions. Future research should focus on developing digital platforms for dynamic scheduling, algorithmizing risk scenarios, and integrating cognitive analytics into construction project management.